{"id":"W4405800330","doi":"10.1109/tro.2024.3521856","title":"Continuous-Time Radar-Inertial and Lidar-Inertial Odometry Using a Gaussian Process Motion Prior","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Robotics","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Odometry; Inertial frame of reference; Lidar; Inertial navigation system; Radar; Computer science; Computer vision; Artificial intelligence; Radar lock-on; Process (computing); Gaussian process; Radar engineering details; Remote sensing; Geodesy; Gaussian; Radar imaging; Geology; Mobile robot; Physics; Robot","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006474402,0.0005800107,0.0004378513,0.0004096047,0.0003570372,0.0007520001,0.0008611197,0.0008155555,0.002245999],"category_scores_gemma":[0.002391116,0.0003571897,0.000436204,0.0007545998,0.0006211326,0.001254541,0.001174019,0.001131908,0.001262603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004806474,"about_ca_system_score_gemma":0.001264172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008158501,"about_ca_topic_score_gemma":0.01168059,"domain_scores_codex":[0.9992736,0.00008302712,0.00002493361,0.0001848644,0.0003594201,0.00007420815],"domain_scores_gemma":[0.999356,0.0001775892,0.00005860986,0.000179389,0.0001925914,0.0000358143],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006203349,0.0003912766,0.004929421,0.0002562789,0.0001074914,0.0003088099,0.0003318024,0.3970433,0.1020875,0.03800538,0.008105982,0.4478124],"study_design_scores_gemma":[0.00002865821,0.00007110868,0.001491836,0.00001036158,0.00001070743,0.0001143682,0.00001820458,0.9703856,0.02077538,0.003541916,0.003527909,0.00002382417],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01757491,0.00007085307,0.9787195,0.0002079126,0.000062829,0.0000318343,0.0001317413,0.001475057,0.001725371],"genre_scores_gemma":[0.4345059,0.0001376627,0.5607534,0.0001845522,0.00005837638,0.00006982961,0.0005373509,0.0001690156,0.003583913],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008158501,"threshold_uncertainty_score":0.01622206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.010583381464063,"score_gpt":0.2327412789017137,"score_spread":0.2221578974376507,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}